[1]} propose a `Trust Score', which estimates the trustworthiness of a prediction by measuring the agreement between a classifier and a nearest-neighbor classifier on a test instance.
In this case, a prediction is trustworthy if it is reasonable in light of the training data, in the sense that the train data behaves in a similar way to the new instance.
As the method we propose for the numeric estimation of trustworthiness is heavily inspired by [1]}, we adopt this definition of trustworthiness: a prediction is trustworthy if it is aligned with the data the model was trained on.
We note that this intuition is also very similar to the idea behind case-based reasoning methods [3]}, [4]}, [5]}.
